基于ConvLSTM网络的维度情感识别模型研究

学业情绪能够影响和调节学习者的注意、记忆、思维等认知活动,情绪自动识别是智慧学习环境中情感交互和教学决策的基础.目前情绪识别研究主要集中在离散情绪的识别,其在时间轴上是非连续的,无法精准刻画学生学业情绪演变过程,为解决这个问题,基于众包方法建立真实在线学习情境中的中学生学习维度情感数据集,设计基于连续维度情感预测的深度...

Research on Dimensional Emotion Recognition Model Based on ConvLSTM Network

Academic emotions can affect and regulate learners’ attention, memory, thinking and other cognitive activities. Automatic emotion recognition is the basis of emotion interaction and instructional decision in intelligent learning environment. At present, the research of emotion recognition mainly focuses on the recognition of discrete emotions, which is discontinuous in the timeline, and cannot accurately depict the evolution process of students’ academic emotions. In order to solve this problem, this paper establishes the dimensional emotional database of middle school students based on the crowd-sourcing method in the real online learning situation. And a deep learning analysis model based on continuous dimensional affective prediction is designed. In the experiment, learning materials that stimulate students’ academic emotions are identified according to students’ learning styles firstly. And then 32 experimenter are recruited for independent online learning and collecting real-time facial images. Next, dimensional database with 157 students’ academic emotion videos and 2 178 students’ facial expressions is obtained by the two-denationalization for each video emotion. Finally, a ConvLSTM net-based dimensional emotion model is established and tested on the dimensional emotion database for middle school students. The mean value of the Concordance Correlation Coefficient（CCC） is 0.581. Meanwhile, the mean value of the uniform correlation coefficient is 0.222 after the experiment on Aff-Wild database. The experiment shows that the dimension-based emotion model proposed in this paper improves the CCC correlation coefficient index by 7.6% to 43.0% in the dimension-based emotion recognition of Aff-Wild database.